Answer:
idk, but depending on that juice i bet they are drunker then hail
Step-by-step explanation:
This is a 2 part question, so we will go step by step
Since Angel makes 13.50 an hour, and she works 40 hours a week, we have to make an equation and solve it.
First, let's make the equation, and we should multiply. Why?
Because finding out how much she made in a week would make it easier to find out how much she makes in a year. To find out how much she makes in a week, we need to add 13.50 40 times, and multiplication is an easier way of doing that.
Now to set it up, 40 is the number we are multiplying by (Because 40 is how many hours she works, and 13.50 is how much she makes in that hour)
and 13.50 is the number getting multiplied
Now we insert numbers into typical multiplication format
13.50 x 40 = ?
Now we solve
13.50 x 40 = 540
So she makes 540 dollars in a week, now we need to multiply that by the number of weeks they are in a year and we are done.
With a little help from Google, we can learn that there are in 52.1 weeks<span> in a common year.
Now we do the same thing we did the last time and insert our numbers into the equation.
(52.1 is what we are multiplying by, and 540 is the number that is getting multiplied.)
540 x 52.1 = ?
Solve
</span>540 x 52.1 = 28,134
She makes <span>28,134 in a year
</span>
Hope this helped!
Answer:
Explained below.
Step-by-step explanation:
Consider the variables height and weight.
It is usually seen that taller people are heavier than shorter people.
So a regression analysis can be used to specify this belief.
The statistical questions that are being asked here are:
- What the independent and dependent variables?
- Are there any other factor influencing the dependent variable other than the independent variable?
The variable <em>Y</em> is considered as the dependent variable and the variable <em>Y</em> is considered as the independent variable. And the main purpose of the regression analysis is to predict the value of <em>Y</em> when the value of <em>X</em> is given.
The linear regression model can be used to predict the past and future value of the dependent variables provided that the independent variables for those times are provided.
